Identification Techniques and Device for Testing the Efficacy of Beauty Care Products and Cosmetics

ABSTRACT

A method for testing the effect of a skin care product includes measuring a mechanical property of skin tissue using nonlinear stochastic system identification, applying the product to the skin, repeating the measurement of the mechanical property after the application of the product, and comparing the before and after measurements to quantify the effect of the product. 
     Measuring the mechanical property of the skin can include placing a probe against a surface of the skin, perturbing the skin with the probe using a stochastic sequence, and measuring the response of the skin to the perturbation. Perturbing the skin can include indenting the skin with the probe, extending the skin with the probe, and sliding the probe across the skin surface. The mechanical property may be indicative of skin compliance, skin elasticity, skin stiffness, or skin damping. The mechanical property can be dependent on perturbation depth and may be measured at a plurality of anatomical locations.

RELATED APPLICATION

This application claims the benefit of U.S. Provisional Application No.61/238,866, filed on Sep. 1, 2009. The entire teachings of the aboveapplication are incorporated herein by reference.

BACKGROUND OF THE INVENTION

Identifying the mechanical properties of skin and other biologicaltissues is important for diagnosing healthy from damaged tissue,developing tissue vascularization therapies, and creating injury repairtechniques. In addition, the ability to assess the mechanical propertiesof an individual's skin is essential to cosmetologists anddermatologists in their daily work. Today, the mechanical properties ofskin are often assessed qualitatively using touch. This, however,presents a problem in twins of passing information between differentindividuals or comparing measurements from different clinical studiesfor the diagnosis of skin conditions.

Studies have explored both the linear and nonlinear properties ofbiological materials. Testing methods used include suction (S.Diridollou, et al., “An in vivo method for measuring the mechanicalproperties of the skin using ultrasound,” Ultrasound in Medicine andBiology, vol. 24, no. 2, pp. 215-224, 1998; F. M. Hendricks, et al., “Anumerical-experimental method to characterize the non-linear mechanicalbehavior of human skin,” Skin Research and Technology, vol. 9, pp.274-283, 2003), torsion (C. Excoffier, et al., “Age-related mechanicalproperties of human skin: An in vivo study,” Journal of InvestigativeDermatology, vol. 93, pp. 353-357, 1989), extension (F. Khatyr, et al.,“Model of the viscoelastic behavior of skin in vivo and study ofanisotropy,” Skin Research and Technology, vol. 10, pp. 96-103, 2004; C.Daly, et al., “Age related changes in the mechanical properties of humanskin.” The Journal of Investigative Dermatology, vol. 73, pp. 84-87,1979), ballistometry (A. Tosti, et al., “A ballistometer for the studyof the plasto-elastic properties of skin,” The Journal of InvestigativeDermatology, vol. 69, pp. 315-317, 1977), and wave propagation (R. O.Potts, et al., “Changes with age in the moisture content of human skin,”The Journal of Investigative Dermatology, vol. 82, pp. 97-100, 1984).

Commercial devices, such as the CUTOMETER® MPA580, DERMALFLEX, andDIA-STRON brand dermal torque meter, exist for some of these methods.Generally, these devices only provide information about limited aspectsof skin behavior which may not be enough to properly diagnose disease.Many of these devices also focus on only linear properties such as skinelasticity.

In another method known as indentometry, (F. J. Carter, et al.,“Measurements and modeling of human and porcine organs,” Medical ImageAnalysis, vol. 5, pp. 231-236, 2001; M. P. Ottensmeyer, et al., “In vivodata acquisition instrument for solid organ mechanical propertymeasurement,” Lecture Notes in Computer Science, vol. 2208, pp. 975-982,2001; G. Boyer, et al., “Dynamic indentation of human skin in vivo:Aging effects,” Skin Research and Technology, vol. 15, pp. 55-67, 2009)a probe tip is pushed orthogonally into the skin to discover tissueproperties. If large enough forces are used, this method is capable ofmeasuring the mechanical properties of not only the epithelial layer,but also the properties of the underlying connective tissue.

The interaction between different tissue layers (C. Daly, et al., “Agerelated changes in the mechanical properties of human skin.” The Journalof Investigative Dermatology, vol. 73, pp. 84-87, 1979; H. Oka, et al.,“Mechanical impedance of layered tissue,” Medical Progress throughTechnology, supplement to vol. 21, pp. 1-4, 1997) is important inapplications like needle-free injection (B. D. Hemond, et al., “ALorentz-force actuated autoloading needle free injector,” in 28^(th)Annual International Conference of the IEEE EMBS, pp. 679-682, 2006),where the dynamic response of skin to a perturbation is important indetermining the required injection depth.

Linear stochastic system identification techniques have been used todescribe a variety of biological systems (M. P. Ottensmeyer, et al.,2001; G. Boyer, et al., 2009; M. Garcia-Webb, et al., “A modularinstrument for exploring the mechanics of cardiac myocytes,” American Jof Physiology: Heart and Circulatory Physiology, vol. 293, pp.H866-H874, 2007). However, many systems cannot be fully described bylinear dynamic models. Investigators have also used nonlinearrelationships to describe the stress strain relationship in skin (F. M.Hendricks, et al., 2003). However, most of this work has been done atlow frequencies and therefore does not describe the dynamic propertiesof skin.

The frictional properties of skin have been studied by several researchgroups (A. F. El-Shimi, “In vivo skin friction measurements.” Journal ofthe Society of Cosmetic Chemists, 28:37-51, 1977; W. A. Gerrard,“Friction and other measurements of the skin surface.” Bioengineeringand the Skin, 3:123-139, 1987; R. J. Hills, A. Unsworth, and F. A. Ive,“A comparative study of the frictional properties of emollient bathadditives using procine skin” British Journal of Dermatology, 130:37-41,1994; S. Nacht, J. A. Close, D. Yeung, and E. H. Gans, “Skin frictioncoefficient: changes induced by skin hydration and emollient applicationand correlation with perceived skin feel.” Journal of the Society ofCosmetic Chemists, 32:55-65, 1981; M. Zhang and A. F. T. Mak, “In vivofriction properties of human skin.” Prosthetics and OrthoticsInternational, 23:135-141, 1999) using custom and commercialinstruments, such as the MEASUREMENT TECHNOLOGIES Skin Friction Meter(Aca-Derm Inc., California). On the other hand, the surface mechanics ofskin includes several properties including skin texture, suppleness, andfriction (Zang and Mak, 1999). When a probe is placed on the skin, theskin can deform contributing to changes in the measured damping (fromskin energy absorption, skin friction and other factors) as well as thespring constant (from skin suppleness, skin stiffness and otherfactors).

Another problem with existing methods is that the dynamics of thetesting device are often not characterized and are assumed to applyperfect forces to the tissue. For example, actuators are assumed to haveperfect output impedance such that the dynamics of the system beingtested do not affect the dynamics of the actuator. In addition, manyexisting methods and devices are limited to one test geometry and oneperturbation scheme. Once a different geometry or testing direction isused, the measured results are not easily comparable.

Trends in consumer skin care have shown the use of specific moleculesand proteins, such as tensin, which are well known to cause collagengrowth or increase skin suppleness in hydration and anti-aging products.Although standard testing devices for skin have been proposed, industryspecialists have expressed dissatisfaction with existing devices.

SUMMARY OF THE INVENTION

A method for testing the effect of a skin care product includesmeasuring one or more mechanical properties of skin tissue usingnonlinear stochastic system identification, applying the product to theskin, repeating the measurement of the mechanical properties after theapplication of the product, and comparing the before and aftermeasurements to quantify the effect of the product.

Measuring the mechanical properties of the skin can include placing aprobe against a surface of the skin, perturbing the skin with the probeusing a stochastic sequence, and measuring the response of the skin tothe perturbation. Measuring the response of the skin can includemeasuring displacement of the skin. Perturbing the skin may includeusing a Lorentz force linear actuator. Perturbing the skin can includeindenting the skin with the probe, extending the skin with the probe,and sliding the probe across the skin surface.

The method may further include modeling the probe and skin as a systemwhere the system includes a linear dynamic component and a non-linearstatic component. Modeling may include generating a non-parametricmodel, generating a parametric model, or both. The non-linear componentcan include a Wiener static nonlinear system. The linear component caninclude a second order mechanical system.

The mechanical properties may be indicative of compliance, elasticity,stiffness, damping, mass, compressible depth, or other nonlinearproperties of skin. The mechanical property can be dependent onperturbation depth and may be measured at a plurality of anatomicallocations.

The present invention has several advantages. Embodiments of theinvention are capable of measuring the mechanical properties of skin ina clinical setting because they are low cost and robust, because theyenable the testing procedure to be fast and accurate, and because theycan be implemented in a hand-held form factor. In addition, devices andmethods disclosed herein are able to fully characterize the dynamiclinear and nonlinear aspects of the mechanical behavior of skin.

A benefit of using an embodiment of the present invention and non-linearstochastic system identification to measure tissue properties is thatmeasurements can be done in viva. Another benefit is that tests can beconducted quickly and each test can obtain as much information aspossible. For example, the devices and methods described herein can beused to characterize the parameters of human skin using nonlinearstochastic system identification, which can be completed within 2 to 4seconds when perturbing the skin using indentation. As an additionalbenefit of using non-linear stochastic system identification, the dataacquisition and analysis method is relatively immune to the movements ofthe patient during the test.

Embodiments of the invention can provide quantitative measurements andmay be used to standardize the qualitative measurements that physicianscurrently use to diagnose tissue diseases. A device with the ability todiagnose tissue diseases (e.g., Scleroderma, Myxoedema, or connectivetissue diseases) or identify the presence of dehydration can have alarge societal impact in healthcare and large market impact in terms oftools that are available to clinicians. Quantitative measurements in aclinical setting can advance the field of tissue mechanics bystandardizing assessments made by different individuals. In addition,devices and methods disclosed herein can be used for understandingmechanics for manufacturing artificial prosthetic tissue, fordetermining mechanical properties in locations that are difficult topalpate (such as in the colon during endoscopy), and determiningparameters needed for needle-free injection.

While different types of tests and devices can be used to identify theanisotropic properties of skin, for in vivo testing, the contributionfrom directions outside the testing plane can affect the results. Thedisclosed devices methods are capable of testing multiple directions, byusing different perturbation modes and interchangeable probe heads,which can be useful in determining these anisotropic materialproperties.

Another benefit is that embodiments of the invention can provide astandardized measurement technique designed to assess the effectivenessof skin care products. The disclosed method can be used to distinguishthe change in skin properties after dehydration or after application ofcommercial products, such as lotions, creams, and anti-aging products.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing will be apparent from the following more particulardescription of example embodiments of the invention, as illustrated inthe accompanying drawings in which like reference characters refer tothe same parts throughout the different views. The drawings are notnecessarily to scale, emphasis instead being placed upon illustratingembodiments of the present invention.

FIG. 1 illustrates a device for measuring tissue properties inaccordance with the invention.

FIG. 2A is a top view of a hand-held version of a device for measuringtissue properties in accordance with the invention. The device isconfigured for indentation.

FIG. 2B is a cross-sectional view of the device taken along line A-A ofFIG. 2A.

FIG. 2C is a perspective view of the device of FIG. 2A.

FIG. 2D is a detail view of detail B of FIG. 2C.

FIG. 3A is a top view of the hand-held version of a device for measuringtissue properties. The device is configured for extension.

FIG. 3B is a cross-sectional view of the device taken along line C-C ofFIG. 3A.

FIGS. 3C and 3D are perspective and front views, respectively, of thedevice of FIG. 3A.

FIG. 4A is a top view of the hand-held version of a device for measuringtissue properties. The device is configured for surface mechanicstesting.

FIG. 4B is a cross-sectional view of the device taken along line D-D ofFIG. 4A.

FIGS. 4C and 4D are perspective and front views, respectively, of thedevice of FIG. 4A.

FIGS. 5A, 5B and 5C illustrate different configurations of a device fordifferent modes of perturbation, including an indentation configuration,extension configuration, and surface mechanics configuration.

FIG. 6 is a schematic diagram of a system for measuring tissueproperties according to the invention.

FIG. 7A shows an example of an input force distribution and FIG. 7B thecorresponding output position distribution for skin under indentationperturbation.

FIG. 8A shows a graph of the power spectral density of the inputvoltage, the measured force, and the measured position of the instrumentduring a nonlinear stochastic measurement of the skin during indentationon the left posterior forearm 40 mm from the wrist.

FIG. 8B shows a graph of the mean squared coherence (MSC) of the inputforce to output position illustrating the frequency ranges that can beexplained by linear elements for MSC near unity.

FIG. 9 shows a Bode plot for the force-to-position relationship forskin. The top panel shows the magnitude relationship and the bottompanel the phase relationship. The cutoff frequency is near 40 Hz forthis configuration of input mass and skin compliance.

FIG. 10A shows the impulse response with parametric fit of the systemshown in FIG. 6.

FIG. 10B shows a plot of measured output against predicted linear outputillustrating a static nonlinearity.

FIG. 11 shows a plot of measured position and predicted position using aWiener static nonlinearity for measuring tissue properties.

FIG. 12 illustrates an algorithm that can be used to solve for Volterrakernels. The algorithm involves constructing the kernel,orthogonalization, solving, resolving, and reconstructing.

FIG. 13 shows plots of Volterra kernels for an example of skinmeasurements using an embodiment of the invention. FIG. 13A shows thefirst order kernel and FIG. 13B the second order kernel without usingnoise-reducing strategies.

FIG. 14 illustrates three basic strategies for reducing the noise inVolterra kernels and the results on the variance accounted for (VAF) inthe measured output.

FIG. 15 illustrates an example of depth partitioned results for skinshowing (A) the partitioning of the output record chosen by data densityand (B) calculated kernels or “impulse” responses at different depths.

FIG. 16A illustrates a real time input generation (RTIG) scheme withoutput probability density function (PDF) feedback.

FIG. 16B illustrates a Real time input generation (RTIG) scheme withoutput PDF feedback and system identification (ID).

FIG. 17 shows identified parameters from real time input generation withPDF feedback and adaptive least squares (ALS) algorithm. Three differentsystems, a linear system, a Wiener static nonlinear system, and adynamic parameter nonlinearity (DPN) system are shown.

FIG. 18 shows plots of the means and standard deviations of fourmechanical properties of skin for two different positions on the leftarm.

FIG. 19A illustrates two locations on the skin tested with extensionperturbation.

FIG. 19B shows skin property measurements obtained at the locationsillustrated in FIG. 19A.

FIG. 20 shows results from skin surface mechanics testing usingindentation with and without lotions applied to the surface.

FIG. 21 shows results from skin property measurements in a surfacemechanics configuration with and without a lotion applied to the skin.FIG. 21A shows plots of the impulse responses and FIG. 21B shows thefitted parameter data for compliance and damping.

DETAILED DESCRIPTION OF THE INVENTION

A description of example embodiments of the invention follows.

The present invention generally is directed to devices and methods formeasuring one or more mechanical properties of tissue, such as the skinof an animal, skin of a fruit or vegetable, plant tissue, or any otherbiological tissue.

Embodiments of the invention use nonlinear stochastic systemidentification to measure mechanical properties of tissue. Nonlinearstochastic system identification techniques have been previously usedwith biological materials, but not to characterize skin tissue. Fordetails of the techniques, see the articles by Hunter and Korenberg 1986(I. W. Hunter, et al., “The identification of nonlinear biologicalsystems: Wiener and Hammerstein cascade models,” Biological Cybernetics,vol. 55, pp. 135-144, 1986; M. J. Korenberg, et al., “Two methods foridentifying Wiener cascades having non-invertible staticnonlinearities,” Annals of Biomedical Engineering, vol. 27 pp. 793-804,1999; M. J. Korenberg, et al., “The identification of nonlinearbiological systems: LNL cascade models.” Biological Cybernetics, vol.55, pp. 125-13, 1986), the entire contents of which are incorporatedherein by reference.

The design of the mechanical device preferably includes an easilycontrollable actuator and force sensing system, a low-cost positionsensor, a temperature sensor, an injection-moldable external bearingsystem and swappable device probes. To minimize the space necessary forthe sensor and the cost of the sensor, a linear potentiometer may beused, although other position sensors can be used, including non-contactLVDTs, encoders and laser systems.

FIG. 1 is a schematic view of a device 100 for measuring tissueproperties according to the invention. Device 100 includes a probe 102configured to perturb the tissue T, an actuator 104 coupled to the probe102 to move the probe, a detector 106 configured to measure a responseof the tissue to the perturbation, and a controller 108 coupled to theactuator 104 and the detector 106. The controller drives the actuator104 using a stochastic sequence and determines the mechanical propertiesof the tissue using the measured response received from the detector106. The controller 108 may include or may be connected to a powersupply 110. The controller can be a microprocessor or a personalcomputer and may be connected to or include a display 112, for example,for displaying a user interface. Actuator 104 preferably is a Lorentzfor linear actuator or voice coil.

Although the device 100 of FIG. 1 is shown in a configuration forindenting the tissue T with probe 102, device 100 may be reconfiguredand used for perturbing the tissue by extending the tissue surface TSwith probe 102, or sliding probe 102 across a tissue surface TS. FIG. 1is shown with the reference surface 39 (see FIG. 2) not in contact withthe tissue surface TS. During operation, the reference surfaces andprobes are in contact with the tissue surface TS.

Although shown as separate elements in FIG. 1, controller 108, powersupply 110, and computer and display 112 may be integrated into anenclosure of device 100, in which case power supply 110 may include abattery. The device 100 can further include a handle 114 for manualapplication of the probe 102 to the surface of tissue T and may includean accelerometer 116 detecting an orientation of the probe 102. Thehandle 114 may serve as an enclosure for holding other parts of thedevice 100, such as controller 108, power supply 110, or accelerometer116. Accelerometer 116 rimy be included in or connected to detector 106.

In some embodiments, detector 106 includes or is connected to a forcesensor 118 detecting force of the perturbation, for example, using acurrent sensor detecting a current input to actuator 104. The detector106 can include a position sensor 120 detecting displacement of thetissue surface TS, for example, using a linear potentiometer, such aspotentiometer 32 (FIG. 2A), which detects the position of probe 102.

In use, the device 100 is typically held by an operator at handle 114.The operator places probe 102 against surface TS of tissue T andtriggers the mechanical perturbation of tissue T through controller 108or with a switch, such as trigger 10 (FIG. 2). The controller 108 thendrives actuator 104 using a stochastic sequence, which may be an inputvoltage or current to actuator 104. Actuator 104 moves probe 102, whichperturbs tissue T.

As illustrated, device 100 is configured for indentation. The front ofthe device faces the tissue surface TS. In this configuration, the probe102 is placed perpendicular to the tissue surface TS and actuator 104moves probe 102 to indent tissue T (see also FIG. 5A). In otherconfiguration, the top of device 100, which can include a referencesurface, such as surface 41 shown in FIGS. 3B and 4B, may be placedagainst the tissue surface TS. Perturbation then occurs with lateralmovement of the probe 102 relative to the tissue surface TS. Lateralmovement of the probe can be used for tissue extension, where the probeis coupled to the tissue surface TS, or for surface mechanics testing,where the probe slides across the tissue surface TS (see also FIGS.5B-C). During or following the perturbation, detector 106 can measure atissue response, which is received by controller 108 for determining amechanical property of the tissue based on the measured response.Results of the measurement may be displayed on display 112. The operatormay repeat the measurement at the same tissue location, or may move theprobe to a different location. Alternatively or in addition, theoperator may change the configuration of device 100, for example fromindentation to extension measurement, reposition the device on thetissue surface TS according to the new perturbation configuration, andperform another measurement at the same location.

Determining the mechanical property can be implemented in hardware andsoftware, for example using controller 108. Determining can includeusing non-linear stochastic system identification and may furtherinclude modeling the probe and tissue as a system comprising a lineardynamic component and a non-linear static component, as shown in FIGS.10A and 10B, respectively. Skin tissue, for example, is a dynamicallynonlinear material. As long as the nonlinearity is monotonic, the systemcan be broken up and analyzed as a linear dynamic component and anonlinear static component. The non-linear component may include aWiener static nonlinear system and the linear component may include asecond order mechanical system, as described in the article by Y. Chenand I. W. Hunter entitled, “In vivo characterization of skin using aWiener nonlinear stochastic system identification method, 31st AnnualInternational Conference of the IEEE Engineering in Medicine and BiologySociety, pp. 6010-6013, 2009, the entire contents of which areincorporated herein by reference.

In some embodiments, using non-linear stochastic system identificationincludes using a Volterra Kernel method. Such a method is described inthe article “The Identification of Nonlinear Biological Systems:Volterra Kernel Approaches,” by Michael J. Korenberg and Ian W. Hunter,Annals of Biomedical Engineering, vol. 24, pp. 250-269, 1996, the entirecontents of which are incorporated herein by reference. Further, themethod may include detecting the force of the perturbation with respectto a reference surface using force sensor 118. Measuring a response caninclude detecting displacement of the tissue surface with respect to areference surface.

Tissue T, FIG. 1, may be produce, i.e., fruits and vegetables, andtissue surface TS may be the skin of a piece of produce. In anembodiment, a method of testing produce includes placing probe 102against skin TS of a piece of produce T, mechanically perturbing thepiece of produce with the probe, measuring a response of the piece ofproduce to the perturbation, and analyzing the measured response usingnon-linear stochastic system identification. Analyzing can includedetermining mechanical properties of the piece of produce T. Themechanical property may be indicative of ripeness of the fruit orvegetable T.

In an embodiment, device 100 may be used to implement a method ofanalyzing mechanical properties of tissue T that includes an outputpartitioning technique to analyze the nonlinear properties of biologicaltissue. The method includes mechanically perturbing the tissue T withprobe 102 using a stochastic input sequence and measuring a response ofthe tissue T to the perturbation, for example using detector 106 todetect position of tissue T. Controller 108 partitions the measuredresponse and generates a representation of the mechanical properties ofthe tissue based on the partitioned response. Partitioning can includegrouping the measured response into position bins over which themeasured response approximates a linear response to the perturbation.Generating a representation can include generating a time-domainrepresentation of the partitioned response, which may include a kernelor an “impulse” response for each position bin. Further, generating arepresentation can includes using orthogonalization of the inputsequence based on the position bins. Additional details of thepartitioning technique are described with reference to FIG. 15.

In an embodiment, device 100 shown in FIG. 1 may be used to implement amethod of analyzing mechanical properties of tissue using real-timesystem identification, such as described elsewhere herein. The methodcan include mechanically perturbing the tissue T with probe 102 using astochastic input sequence, which may be generated in real time,measuring a response of the tissue to the perturbation, for exampleusing detector 106, analyzing the measured response, and adjusting theinput sequence based on the analysis while perturbing the tissue.Analyzing and adjusting, which can include adjusting the input sequencein real time, may be implemented in controller 108. In analyzing themeasured response, the controller 108 may implement algorithms forobtaining a distribution of the measured response and for performingnon-linear stochastic system identification.

FIGS. 2A-4D show a hand-held version of an embodiment of device 100 formeasuring tissue properties. FIGS. 2A-2D show device 100 in anindentation configuration, FIGS. 3A-3D in an extension configuration,and FIGS. 4A-4D in a surface mechanics testing configuration.

As shown in FIG. 2A, device 100 includes probe 102, actuator 104 coupledto the probe 102 to move the probe, detector 106 configured to measure aresponse of the tissue to the perturbation, and handle 114.

FIG. 2B is a cross-sectional view of device 100 taken along line A-A ofFIG. 2A. Handle 114 includes handle base 4 that houses trigger button 10and internal wires 33, which connect trigger button 10 to connector plug11. Connector plug 11 can provide for electrical connections to anexternal controller 108, computer 112, and power supply 110 (FIG. 1). Ahandle cover 5 (FIG. 2C) can be attached to handle base 4. Handle cover5 can be supported by handle cover mounting standoffs 6 and secured inplace by, for example, screws inserted in mounting holes 7. Handle base4 can be attached to applicator body 12 via a sliding attachment 8 andsecured via mounting holes 9 and screws.

Applicator body 12 provides an enclosure which fixtures the actuator104, the position sensor 106, one or more position reference surfaces,e.g., reference surface 39 (FIG. 2D), and internal wiring guides, e.g.,for electrical wires 32 and 33. The body 12 of the device doubles as anencasement for a magnet structure 21 and as a bearing surface for abobbin 23. Teflon bearing spray may be used to reduce static frictionand help create constant dynamic damping. The applicator body 12 may beinjection-molded and made from plastic.

As shown in FIG. 2B, actuator 104 is a Lorentz force linear actuator orvoice coil—that includes an iron core 20 to guide the magnetic field,magnet 21, steel plate 22, bobbin 23, and coil 26, which may be madefrom copper.

Lorentz Force Linear Actuator

The Lorentz force is a force on a point charge caused by anelectromagnetic field. The force on the particle is proportional to thefield strength B_(e) and the current I* that is perpendicular to thefield multiplied by the number N_(e) of conductors of a coil in serieseach with length L_(e). When a current is applied to the coil 26, thecharges interact with the magnetic field from the permanent magnet 21and are accelerated with a force. An actuator which is designed to applya force directly (rather than through force feedback) is desirable forhigh-bandwidth operation. In addition to speed, a Lorentz force actuatorallows for a large stroke in order to test the depth dependentnonlinearities in skin, for example when using an indentation probe.

The power handling capabilities of a coil are limited by its heatgeneration (due to ohmic heating) and its heat dissipation capabilities.The housing for many coils provides heat sinking abilities preventingthe coil from heating too quickly. In addition, a moving coil canprovide convective cooling. The most commonly used forces and testlengths for this device would not require advanced heat handlingmeasures but a temperature sensor 28 can be added as a safety measure tomonitor the temperature for high force or extended length tests.

In addition to the relatively simple operating principles, the Lorentzforce linear actuators where chosen for the following reasons.

-   -   Direct force control: The Lorentz force coil can be driven to        produce a force as commanded since current is proportional to        force and voltage is proportional to velocity. Forces less than        15 N would require that the actuator used in tissue testing be        driven at voltages lower than 48 V. Directly controlling force        open loop gives advantages for proving the identifiability of        system parameters when compared to servo-controlled stages.    -   Incorporated force sensing: The force can be measured by looking        at the current flowing through the actuator. This can be the        most low-cost method for measuring force. For some separate        force sensors, there are additional dynamics, which are        detrimental to the system identification process.    -   High force limits: The coil can be driven to high forces which        may be limited by the amplifier and the heat transfer properties        of the actuator.    -   Long stroke: The coil can be designed with a long stroke with        relatively large regions of linear operation. Other systems like        those driven by piezo-electrics do not have as high a stroke and        do not generally operate at low voltages.    -   High bandwidth: The bandwidth of a Lorentz force coil is limited        by input power, the mass of the system, and the stiffness of the        tissue being tested. Other actuation strategies, such as lead        screw systems, have relatively low bandwidth in comparison.    -   Low cost: The actuator consists of a magnet, a steel cap, an        iron core to guide the magnetic fields, and a copper coil. The        simplicity of the design helps reduce cost.    -   Few accessories necessary: In order to operate the coil, the        only accessories outside the actuator can be an amplifier and        power system. Other perturbation strategies, such as non-contact        pressure systems, require an additional high pressure pump and        valves.

Device Construction

In accordance with an embodiment of the invention, severalimplementations of devices were constructed, including a desktop deviceand a hand-held device, such as device 100 shown in FIGS. 2A-4D. Tominimize the space necessary for the position sensor and the cost of thesensor, a linear potentiometer can be used.

According to an embodiment of the invention, a desktop version (notshown) of device 100 includes a Lorentz force linear actuator, such asactuator 104, with a bobbin mass of 60 g, a total length of 32 mm, andan inner diameter of 25.2 mm. To construct the actuator, a magnetstructure (BEI Kimco Magnetics) was used with a neodymium magnet with amagnetic field strength of 0.53 T. A custom designed overhung bobbin,such as bobbin 23, was 3D printed with multiple attachments for atemperature sensor, such as sensor 28, easily insertable electricalconnections, through holes, such as holes 35, to allow air flow,threaded holes, such holes 34, for attachment of custom probe tips orheads, and a wire insertion slot. In one embodiment, the custom woundcoil has a resistance of 12Ω, inductance of 1.00 mH, and 6 layers ofwindings using 28 gage wire.

Embodiments of device 100, in both the desktop and handheld version,include a force sensing system via a current sense resistor. The coildesign also includes the integration of a small temperature sensor 28,such as OMEGA F2020-100-B Flat Profile Thin Film Platinum RTD, into theside of the coil 26. This RTD monitors the temperature of coil 26 toprevent actuator burn-out. A low-cost linear potentiometer 29, such asALPS RDC10320RB, can be used to measure position. When implemented withan amplifier and 16-bit DAQ the position resolution is as low as 0.5 μm.

In the desktop version of device 100, attachment 8 allows applicatorbody 12 to be slid into modular aluminum framing (MK automation) insteadof a custom handle, such as handle 114 of the handheld version of thedevice shown in FIG. 2. For indentation, the desktop version of thedevice can utilize gravity to provide an extra constant preload on thesurface of the tissue.

The framing attached to a base allows the testing system to have a smalland stiff structural loop that enables device 100 to be more precise andhelps eliminate system noise. The more important structural loop,however, is the one between the rim of the actuator, such as referencesurface 39, and the system or tissue being tested. This is becauseforces and positions are being measured with respect to the referencesurface, thereby allowing device 100 to characterize tissue compliance.

The handheld system is typically smaller than the desktop system andtherefore has a lower force output. A handheld version of device 100 wasconstructed according to an embodiment of the invention. In oneembodiment, the stroke of the actuation system of the handheld device isnominally 32 mm, the bobbin resistance is 9.5Ω, and the magnetic fieldstrength is 0.35 T. In addition, the mass of the actuator, for example,is 39.5 g and the total mass of the handheld device, for example, is 256g.

As long as the reference surface, such as reference surface 39 or 41, isplaced in firm contact with the surface of the patient's skin, thesystem will successfully measure the compliance transfer function of theskin and not of other components. To account for force changes whengoing from measurements in the desktop system with respect to thehandheld system, two additional components can be added. A bracing bandmay be used to help maintain the lateral position of the actuator on theskin. An accelerometer may also be added to account for the orientationof the handheld version of device 100 and to compensate for thedirectional loading differences from gravity.

Device Configurations

The design of the instrument allows for several different in vivo systemidentification modes including probe indentation, extension, and surfacemechanics testing depending on the custom probe type used. FIGS. 5A-5Cillustrate the different configurations for device 100. Small insetsshow examples of probe heads suitable for each test configuration.

For indentation, FIG. 5A, a typical tip used for contacting the skin isa 4.4 mm steel disk with a normal thickness of 1 mm. For example, asmaller probe tip having a 4.3 min diameter and 0.4 mm corner radius ora larger probe tip having a 5 mm diameter and 1.3 mm corner radius mayalso be used. FIG. 2D shows indentation head 38 including tip 36 mountedto bobbin 23 of device 100 via probe mount 37. Tips with differentthickness, diameters, and corner radii can also be used. The attachmentto bobbin 23 of the actuator is recessed to allow the skin to freelyconform without contacting other parts of the probe. All indentationmeasurements, such as position or displacement of the tissue, are madewith respect to the position reference surface 39 that is significantlylarger than the circle of influence from probe tip 36. The measurementof position of the probe tip relative to the reference surface isindicated by the arrow labeled y in FIG. 5. For indentation into theskin, e.g., pushing normal to the skin surface, no taping or gluing isnecessary. However, to do experiments that require lifting or pullingthe skin, the probe must be coupled to the skin surface using, forexample, suction, liquid bandage, or some other type of mild glue.

For extension experiments, FIG. 5B, the probe moves laterally, asindicated by arrow y, relative to the tissue surface and another probehead and reference surface are used. FIG. 3A-3D show a handheld device100 configured for extension testing. In one embodiment, the extensionprobe head 40 is 5 mm by 16 mm with rounded edges that have a 2 mmradius. The rounded edges, also shown in FIG. 5B, are important forreducing stress concentrations. The reference surface 41 can be a flatface on the body 12 of device 100. It serves as the second probe surfaceand measurements of skin displacement are made relative to the referencesurface 41. This is illustrated in FIG. 5B by arrow y. Coupling to theskin can be provided by a normal, i.e., perpendicular, preload and amild layer of liquid bandage or double-sided tape. The extension systemcan be oriented along the Langer's lines or in other orientations tocharacterize anisotropic tissue properties.

Surface mechanics testing, FIG. 5C, or friction assessment, isconfigured in a manner similar to extension experiments except that theprobe is more rounded and allowed to slide along the surface of theskin. Since the surface mechanics system is second order with a pole atthe origin, an external spring is needed in the system to completelinear stochastic system identification. FIG. 4A-4D show a handhelddevice 100 configured for extension testing. Surface mechanics probe orhead 42 includes probe tip 43 and spring 45. Also included may be linearguides 46 and force sensor 44. The measured compliance will be afunction of the depth of the compression of the probe into the skindepending on the vertical preload.

System Model

FIG. 6 shows the schematic diagram of a system for measuring mechanicalproperties of tissue, e.g., skin tissue, along with inputs and outputs.The system includes the actuator and tissue dynamics and is based on theuse of a Lorentz force linear actuator. The actuator, such as actuator104, has an inherent mass and the bearings and air resistance haveinherent damping. There are two possible methods for treating thenonparametric or parametric information that can be derived from theidentification of this system. Either the system can be treated as awhole (e.g. the derived mass is a system mass) or the system can betreated as a linear addition of actuator parameters and tissueparameters (e.g., the derived mass is composed of the actuator massacquired from calibration plus the tissue mass). The results givenherein are values of the system, which includes both the actuator andtissue, because the actuator, such as actuator 104, cannot be consideredto have an isolated input impedance.

In FIG. 6, the input is a voltage V*(t) sent through a linear amplifier,such as a Kepco BOP 50-8D amplifier, into the force actuator thatperturbs the skin. The applied force is F*(t) and the position is P*(t).Because of different sources of sensor error e₁(t) and e₂(t), themeasured force F(t) is based on the current I*(t) and the measuredposition is P(t). The sensors include a current sensor that measures theforce output F(t) of the Lorentz force actuator (since current and forceare linearly related) and a linear potentiometer to measure the positionP(t) of the probe tip. Note that the input generation component isseparate from the data acquisition software. Therefore, this systemoperates open loop. Closed loop input generation can be implemented whenthe loop is closed between data collection and input generation, asdescribed herein with reference to FIG. 16. Data acquisition may becontrolled by a NATIONAL INSTRUMENTS USB 6215 device. A data acquisitionsoftware environment, such as LABVIEW 8.5, can be used to implement thecontrol program and user interface.

The force measurement is typically taken after the amplifier, FIG. 6,for several reasons. First, measuring the current after the amplifierskips the amplifier dynamics and any output timing lags of the software.No matter if the input is a voltage or current command, measuring thedynamics after the amplifier is desirable. Second, a force todisplacement measurement would create a causal impulse response of themechanical compliance, which can be analyzed with simpler systemidentification techniques. Lastly, because the input to the system isdirectly related to the force output, as opposed to a position-basedactuator system, an internal feedback algorithm is not needed. Thiscreates a mathematically simpler system identification situation withthe capability to act at higher frequencies; a system with feedback istypically required to operate at a significantly lower frequency thanits controller/observer poles and zeros. Because of the configuration ofthe system, a real-time controller is not necessary for operation butcan still be implemented for real-time input generation schemes.

Linear and Static Nonlinear Techniques

Skin tissue is a dynamically nonlinear material. As long as thenonlinearity is monotonic, a system as shown in FIG. 6 can be broken upand analyzed as a linear dynamic component and a nonlinear staticcomponent using relatively simple cascade techniques. The linear dynamiccomponent and the static nonlinear component of tissue can be identifiedusing white, Gaussian inputs or non-white, non-Gaussian inputs. In orderto identify the static nonlinearity, the predicted linear output isfirst calculated from the convolution of the nonparametric impulseresponse and the input. A free constant can be moved between the dynamiclinear and static nonlinear estimates. The dynamic linear component canbe normalized by dividing the impulse response by the DC compliance.Lastly, the linear and nonlinear identification algorithms may beiterated to create a better overall estimate. Details of the techniqueare described in the article by Y. Chen and I. W. Hunter entitled, “Invivo characterization of skin using a Wiener nonlinear stochastic systemidentification method, 31st Annual International Conference of the IEEEEngineering in Medicine and Biology Society, pp. 6010-6013, 2009; and inthe article by I. W. Hunter and M. J. Korenberg, 1986.

The exemplary results presented in FIGS. 7-10 were obtained from skintissue in vivo with the desktop version of device 100 using anindentation configuration as, for example, shown in FIG. 5A. Theexperimental procedure for obtaining data involves first checking thearea of the skin for markings or signs of wear. The tip of the device isthen lowered to the skin surface with a predefined preload for 0.5seconds. The value of the preload is equivalent to the average of theforce that is later applied during the test. The system identificationinput is tailored such that the coil is always in contact with the skinand that the maximum force is less than 10 N to 12 N. Results wereobtained from an area of the posterior forearm 40 mm distal from thewrist.

A perturbation input with an appropriate cutoff frequency is firstdetermined followed by linear and nonlinear system identification. Thena parametric model is fitted to the data and the repeatability of theresults can be assessed, as described below.

Input Filtering

Stochastic inputs can have a variety of distributions and colors.Examples are Gaussian white inputs or Brownian process inputs, which arethe workhorses of classical system identification methods. When aGaussian distribution is put into a linear system, the output is aGaussian distribution. When other distributions are put into a linearsystem, the output distributions will change and will often approach aGaussian. For nonlinear systems, however, an input Gaussian may turninto another distribution, as shown in FIG. 7 for an exemplary inputforce and output position distribution of skin under indentation.

The stochastic sequence used to perturb the tissue can be tailored to arange of input frequencies. An input sequence characterized by inputcutoff frequency of approximately 200 Hz, implemented with an 8th orderButterworth filter, can give an optimal balance between displacementrange, dynamic bandwidth and the noise floor. In one example ofmeasurements, a sampling frequency of 2 kHz was used with a test lengthof 4 seconds. As little as 2 seconds may be needed to obtain sufficientdata for system identification.

FIG. 8 shows the resulting power spectral density of the input voltage,the measured force and the measured position as well as the coherencesquared between the force and the position output of the system in theexample. At lower frequencies the system exhibits behavior that may notbe explained with a simple linear model since the coherence is less than1.0.

Linear System Identification

FIG. 9, which shows the Bode plot of the system, can be calculated fromthe ratio of the cross-power spectrum to the input power spectrum. Thecutoff frequency appears near 40 Hz for this configuration of input massand skin compliance. The impulse response of the system is determinedusing the method outlined above and as described in the articles by Chenand Hunter 2009, and Hunter and Korenberg 1986. FIG. 10A shows theimpulse response of the system. It can be deduced from the impulseresponse and from the Bode plot that the system has a second ordertransfer function. A parametric model can therefore be created to obtainintuition about the system.

In this mechanical system, FIG. 6, there is an effective mass M_(e)(contributions from coil mass and effective inertia of the skin),effective damping B_(e) (contributions from friction, eddy currentdamping, skin damping) and effective spring constant K_(e)(contributions from average skin stiffness). This produces a secondorder transfer function in the Laplace domain of the form shown inEquation 1 when the input is current driven.

$\begin{matrix}{\frac{P(s)}{F(s)} = {\frac{1}{{M_{e}s^{2}} + {B_{e}s} + K_{e}}.}} & (1)\end{matrix}$

The impulse response of the second order transfer function can be fittedto the measured data. Before dividing by the DC compliance, theeffective mass in one example was found to be 0.0912 kg (the measuredprobe and bobbin mass was 0.060 kg), the effective damping was found tobe 22.77 Ns/m, and the effective spring constant was found to be 4.67kN/m. The impulse response can then be convolved with the input tocreate a predicted linear output. The variance accounted for (VAF) ofthe nonparametric model in the example is 75.79%. The VAF of the secondorder transfer function (parametric model) is 75.64%.

Wiener Nonlinearity

The predicted linear output can be plotted against the measured outputto show a static nonlinearity as is shown in FIG. 10B. The measuredoutput, or the depth into the skin in millimeters (mm), is shown mappedagainst the predicted linear output in newtons (N). The position shownon the y-axis is the absolute position on the actuator. This is used sothat the nonlinearity can be mapped against possible nonlinearities inthe actuator to show that the actuator nonlinearities are negligible.The surface of the skin is located at 10 mm on the actuator for thistest. Positive values of the predicted linear output indicate forcespressing into the skin and negative values indicate pulling off of theskin. An additional contribution of 0.59 N was added during calibrationto account for the contribution of gravity.

After subtracting out the baselines of the data in FIG. 10B, a fit canbe obtained of the nonlinearity in the form shown in Equation 2, where zis the predicted linear output (after several iterations) in newtons andg(z) is the nonlinear function in mm to fit y, the measured output. Thereason for the change in units is because the nonlinear function waschosen to carry the significant units when the linear dynamics arescaled by a set constant. In this form, the change in stiffness as afunction of depth can be represented completely by g(z).

y=g(z)=C ₁(1−e ^(−C) ² ^(z)).  (2)

In this function, the parameter C1 is a measure of the totalcompressible thickness of the skin and underlying tissue while C2 can beinterpreted as the constant that determines the stiffness of thematerial at different depths.

When the nonlinear model is convolved with the original input of theexample measurements, a predicted non-linear output can be obtained asshown in FIG. 11. The resulting VAF is 80.7%, an increase of about 5%over the linear model prediction, indicating that the system formeasuring tissue properties, such as those of skin, can be betterexplained by the nonlinear model.

Volterra Kernel Techniques

Instead of using iterative techniques to identify static nonlinearitiesin an actuator and tissue system, such as Wiener nonlinearitiesdescribed above, a comprehensive technique for determining the Volterrakernels can be use. The Volterra series is a functional expansion of thegeneral time-invariant nonlinear dynamic system problem. The idea behindthe functional expansion is that the zeroth order kernel represents thesystem average. The first order kernel represents the first order linearperturbation to the system where the output depends linearly on laggedinputs. This kernel is exactly the linear impulse response function. Thesecond order kernel represents the second order perturbation to thesystem where the “impulse response” function is not a function of onelag but a function of two lags. This means that the input at some timecan interact with the input at another time to produce an effect on theoutput. This concept can be expanded to higher orders. For a system witha finite memory length I, the discrete functional expansion can bewritten,

$\begin{matrix}{{y_{s}(n)} = {h_{0} + {\sum\limits_{i = 1}^{I_{2} + 1}{{h_{1}(i)}{x\left( {n - i} \right)}}} + {\sum\limits_{i_{1} = 1}^{I_{3} + 1}{\sum\limits_{i_{2} = 1}^{I_{3} + 1}{{h_{2}\left( {i_{1},i_{2}} \right)}{x\left( {n - i_{1}} \right)}{x\left( {n - i_{2}} \right)}}}} + \ldots + {e(n)}}} & (3)\end{matrix}$

Equation 3 allows for memory lengths I₁, I₂, I₃, etc., to be differentfor different kernels. Memory lengths I₁, I₂, I₃, etc., can be the sameand may be equal to memory length I. As it stands, the Volterraexpansion is difficult to solve; one of the reasons is that theexpansion contains many parameters in h₁, h₂, etc. which grow veryquickly with the memory length and kernel order. Secondly, the system isnot orthogonal so changing one value will change the optimal fit forother values in the series.

The Volterra kernel is, however, only one functional expansion amongmany for nonlinear dynamic systems. A modification to the Volterrakernel developed by Norbert Wiener attempts to make solving the systemmuch simpler. The Wiener functional expansion orthogonalizes theVolterra series for an assumed form on the input. By using assumptionsfor Gaussian white inputs, Wiener was able to create a differentexpansion such that the first kernel can be solved independent of thesecond kernel. This means that any noise remaining after solving thefirst order kernel must either be noise or components of higher orderkernels. It is important to note that the Wiener and Volterra kernelsolutions are not exactly the same. The zeroth order Wiener kernels arethe mean output for one type of Gaussian white input. The first andsecond kernels, however are the same for the two systems as long asthere are no higher order kernels.

Several Wiener kernel solution techniques exist includingcross-correlation methods, repeated Toeplitz matrix inversiontechniques, and use of functional expansions. The drawback of the Wienerfunctional expansion is that only white inputs can be used. Since realinputs can only become white asymptotically, there is inherentuncertainty in the solutions for short test lengths. In addition, theinput to a real system is rarely optimally Gaussian and white. It ispossible to create orthogonal expansions for different types of inputsbut not every mathematical function has properties that would allow thisto be readily accomplished. In addition, it becomes cumbersome to dosystem identification if a new expansion needs to be derived for everynew input.

There are several different methods that can be used to solve Volterrakernels. The method developed by M. J. Korenberg and I. W. Hunter, “Theidentification of nonlinear biological systems: Volterra kernelapproaches, “Annals of Biomedical Engineering, vol. 24, pp. 250-268,1996, can be generalized to any input. It imposes no constraint on theinput type (input does not need to be Gaussian and white to be solved),length, or smoothing constraints used on the kernels. Because of thebenefits of the Korenberg and Hunter method, it is used as the basis fortechniques described herein for obtaining results, for example, for skinunder indentation. Because this method requires a few modifications forthe input types used in this work, additional implications and methodsfor obtaining interpretable kernel data are described.

Exact Orthonormalization Solver

The method by Korenberg and Hunter requires an exact orthonormalizationstep for the input, a solution step in the orthonormalized space, and areconstruction step to take the solution back into the space of theVolterra kernel. A summary of the solution steps are shown below andillustrated in FIG. 12:

1. Construct: Sort the input data according to a set of rules.

2. Orthonormalization: Use a modified Gram-Schmidt solver toorthonormalize the input data.

3. Solve: obtain an orthonormalized solution.

4. Resolve: use the inverse of the Gram-Schmidt process to put thesolution back into its original terms.

5. Reconstruct: use the same partitioning rules to resolve the kernelresponses.

Steps 2 through 4 for the orthonormalization technique are well known.In fact, Gram-Schmit orthogonalization is the exact step used by Wienerto orthogonalize the Wiener kernels. Steps 1 and 5 are dependent ondifferent partitioning rules, which need to satisfy constraints from themodified Gram-Schmidt orthonormalization process. The termsorthonormalization and orthogonalization are used interchangeably here.

A modified version of the technique used by Korenberg and Hunter wasdeveloped. This process can be optimized for computational speed in, forexample, the MATLAB technical computing environment, and also fornumerical stability.

Many physically realizable, finite memory systems can be modeled from aninput output relation that is shown,

$\begin{matrix}{{y(n)} = {{\sum\limits_{m = 1}^{M}{A_{m}{P_{m}(n)}}} + {{e(n)}.}}} & (4)\end{matrix}$

In the simplest linear case, y(n) is the output of the single inputsingle output system, A_(m) is the impulse response of the system withmemory M and P_(m)(n) (which is not position in this case) is simplyequal to the input x(n−m−1). The measurement contains some error e(n).From this form, one can easily obtain a linear input output relation. Inthe more general case, m is a value, which stores the dynamic memory ofthe system, which stores input lag information while n represents thevalue at a given time. The P_(m)(n), however stores information for aparticular set of rules that apply at a given m and n. This implies thatA_(m) is a series that is convolved with P_(m)(n) to produce the desiredoutput where P_(m)(n) is constructed based on some partitioning rule. Itis typically difficult to directly solve this equation and thereforeneeds to be orthonormalized into a different form in terms of variablesγ_(m) and β_(m),

$\begin{matrix}{{y(n)} = {{\sum\limits_{m = 1}^{M}{\gamma_{m}{\beta_{m}(n)}}} + {{e(n)}.}}} & (5)\end{matrix}$

A detailed description on how to solve the above equation, includingexemplary computer code, is given in Korenberg and Hunter 1996.

Exemplary Results and Post-Processing Using Volterra Kernels

Using the techniques outlined, Volterra kernels were used to analyzeexperimental data from skin subjected to indentation. The input issampled at 2 kHz and the cutoff for the input is an 8^(th)-orderButterworth at 200 Hz. While several distributions and inputs weretested, a uniform distribution was used except where indicated and auniform input was used because it does a better job of exploring therange of the nonlinearity than a Gaussian input. The input memory lengthhas been shown to be around 250 samples for this sampling rate. Sincethis would result in an extremely long computational time, theinformation was initially downsampled by 3 to reduce the number ofparameters and the computation time.

FIG. 13 shows plots of Volterra kernels for an example of skinmeasurements using an embodiment of the invention. FIG. 13A shows thefirst order kernel and FIG. 13B the second order kernel. Note that theVolterra kernel can be represented in terms of either samples orseconds. The first kernel, FIG. 13A, looks very much like the expectedimpulse response. The relative size of the first order kernel comparedto the second order kernel is also shown in FIG. 13A. Most of the datacan be explained by the first kernel and the first kernel has a muchlarger magnitude than the second kernel contribution.

The second kernel, FIG. 13B appears to have no discernible pattern. Infact, the oscillations are so violent that there are nodes of noise thatgo in equal magnitude towards positive values and negative values. Thisleaves the second kernel completely uninterpretable. It is possible todetermine the estimated output by reconstructing the values of A_(m) andP_(m). This result shows that the VAF is very high at a value of 96% orbetter with a corresponding AIC of 1171.

Several methods can be used to produce smoothing in the second Volterrakernel so that it is more easily interpretable. Since the second orderkernel has already been obtained, however, there are other methods thatcan be used to impose smoothing constraints in post-processing. Withthese processes, it is possible to look at the kernel before smoothingto determine if smoothing is necessary and then choose the appropriatesmoothing technique afterwards. It is also possible to compare thegoodness of fit before and after smoothing.

Post-Processing Techniques

FIG. 14 illustrates the three basic strategies for reducing the noise inVolterra kernels and the results on the variance accounted for (VAF) inthe measured output. Post-processing techniques include downsampling,using a two dimensional smoothing filter, or using a white input signal.All of these techniques help make the second Volterra kernel moreinterpretable by smoothing the kernel. Downsampling is undesirablebecause one must downsample until the input is essentially a whiteinput. With this technique, a lot of high frequency data is lost. In theexample shown in FIG. 14, the information is downsampled by a factor of5. A white input signal is also undesirable because it imposes a fixedstructure on the input signal. A two-dimensional filtering technique(for example with a Gaussian distribution with a spread of 15 samples)is generalizable to any set of inputs and any choice of downsampling;the major disadvantage being the loss of the fitting ability of thesecond order kernel. For an original VAF of 94.8%, an input filter canreduce the VAF to 90.4%, downsampling can increase the VAF to 96.4% (butthis is due to the fact that downsampling reduces the number of datapoints), and using a white signal maintains a high VAF comparable to theoriginal as shown in FIG. 14.

By choosing post-processing techniques to look at the second Volterrakernels, it is possible to compare the original kernel with thepost-processed kernel and make an informed decision about the shape ofthe nonlinearity. Although there are practical restrictions to the inputsignal for the algorithm presented, additional post-processing hashelped overcome some of them. The heart of the algorithm is based on aGram-Schmidt orthonormalization technique, which can be used to solvenot only Volterra kernels but also any other type of basis function aslong as certain conditions are met. There are several applications ofthis algorithm including different basis functions and partitioningsystems.

Systems with different stiffness and damping can be compared, to firstorder, by comparing the relative values of different peaks in the firstand second Volterra kernels. It is worth mentioning that this is aqualitative comparison and that it may be difficult for clinicians tocompare these values graphically. It may therefore be desirable to beable to do a quantitative comparison with fewer parameters or usingdifferent representations.

Partitioning

Partitioning techniques are nonlinear system identification techniquesbased on the Gram-Schmidt orthonormalization solver described above withreference to FIG. 12 (see, e.g., Korenberg and Hunter 1996). Themotivation for using a partitioning technique for stochastic systemidentification follows from a few desirable characteristics: shorttesting length, physical interpretability (easily quantitativelycomparable) and flexible model structures. The concept behind thetechnique is to break up the input signal into groups based on a set ofrules such as the output level, the direction of the signal (whether itis going in a positive or negative direction), or some fuzzy logic-basedinput and output rules. Then, by using direct orthonormalization as asolver, the “impulse” responses or kernels for each of these rules canbe obtained.

The use of stochastic inputs is desirable because the technique queriesmultiple frequencies at once. When using stochastic inputs withnonlinear systems, there are several ways to approach the initialidentification. In one approach, the user can use a small perturbationrange since most physical systems can be linearized for small regions.This localized linear technique, however, will require multiple separatetests or will require the user to add a ramp or sine function to thestochastic signal during the test. After the test, the user will have todo linear system identification on small chunks of locally linear data.This process can be slow and it is desirable to use a faster technique,such as partitioning, that can obtain linear and nonlinear informationat the same time.

Depth Dependent Partitions

The formulation for depth dependent partitions, one of the preferredembodiments, assumes a monotonic static nonlinearity and is notapplicable for non-monotonic nonlinear functions. The basic idea behindthis representation is that the dynamics of a system change as afunction of depth into the skin and that the dynamics do not have aparticular pattern that must be matched by all the constituents. Forexample, this means that the effective mass, damping, and springconstants, M_(e), B_(e), and K_(e), do not have to evolve with the sameunderlying static nonlinearity. Data from different depths is looselygrouped together and an overall “impulse” response or kernel for thatgroup is given. This is similar to the idea of completing localizedlinear system identifications with inputs of smaller ranges.

A key difference between this technique and simply completing localizedlinear system identification is that this technique imposes theseparation of the different depths after the data over the entire outputrange is collected. This means that it can be used to artificially groupnon-contiguous sections of data that are collected at the same depth inorder to make an estimate of the dynamics.

It is expected that the “impulse” responses from these partitionedkernels would not produce results that look exactly like the resultsobtained from localized linear tests. The main reason is because thelocalized linear techniques contain little or no data for cross dynamicterms between different depths. The depth dependent partitioning,however, does contain cross dynamic terms that will cause some averagingto occur across the kernels. In the case where there are no crossdynamic terms, however, the localized linear and depth dependentpartitioning techniques would produce exactly the same results. Othermore advanced types of partitioning schemes can be used to separate thecross dynamic terms.

The method used to solve for the partitioning technique involves usingthe Gram-Schmidt Orthonormalization technique described with referenceto FIG. 12. The construction and reconstruction steps, however, arereplaced by the equations listed below. The construction equation forthe depth dependent partitions listed below would replace thecorresponding equations for Kernel construction in Korenberg and Hunter1996 in the solution process,

$\begin{matrix}{{P_{m}(n)} = \left\{ \begin{matrix}1 & {{{for}\mspace{14mu} m} = {{1\mspace{14mu} {and}\mspace{14mu} n} = {I + {1\mspace{14mu} \ldots \mspace{14mu} N}}}} \\{x\left( {n - j + 1} \right)} & {{{{for}\mspace{14mu} m} = {2\mspace{14mu} \ldots \mspace{14mu} {K_{\max}\left( {I + 1} \right)}}},} \\\; & {{n = {I + {1\mspace{14mu} \ldots \mspace{14mu} N}}},} \\\; & {{{and}\mspace{14mu} j} = {{1\mspace{14mu} \ldots \mspace{14mu} I} + 1}} \\\; & {{{when}\mspace{14mu} {L(k)}} \leq {y\left( {n - j + 1} \right)} \leq {L\left( {k + 1} \right)}} \\\; & {{{{for}\mspace{14mu} k} = {1\mspace{14mu} \ldots \mspace{14mu} K_{\max}}},}\end{matrix} \right.} & (6)\end{matrix}$

where K_(max) is the total number of partitions, k is the partitioncounting variable and L(k) is the partition breakpoint. The mostimportant criteria necessary for generating a partition scheme is thatthe construction equation must be orthogonal. This means that therecannot be overlapping segments or repetition of any segments. Forstability and noise rejection, the output y used for the construction ofthe kernels can be low-pass filtered while they used for the solutionsteps are not altered.

The following equation would then be used to replace the correspondingequations for kernel reconstruction in Korenberg and Hunter 1996 in thesolution process for reconstruction. This equation contains informationfor different partitioned kernels, each of which is similar to an“impulse” response. The total number of these kernels is equal to thenumber of partitions K_(max),

ĥ _(p)(i,j)=A(m) for i=1 . . . K _(max) and j=1 . . . I+1 wherem=j+(i−1)(I+1).  (7)

The number of partitions can be chosen to be any value up to the fittinglimit. This means that the total number of parameters (which is equal tothe number of partitions multiplied by I+1) must be at most one third ofthe total test length.

The partitioning breakpoints can be set based on several differentcriteria. The first obvious criteria would be equal partitions whereeach partition covers the same distance. Realistically, this means thateach partition would have a different number of data points which wouldthen cause some partitions to have too few data points for properfitting.

Another possible partitioning breakpoint algorithm would involvechoosing breakpoints such that each partition has the same number ofdata points within it. This avoids the problem of having too few datapoints in any partition but causes some averaging effect to occur. Thisis the type of breakpoint that is used in most of the followinganalysis. The algorithm used to generate these breakpoints involvesputting the entire output data series in order from the lowest value tothe highest value. The break points can then be chosen to split thenumber of data points evenly. The depth value that would correspond tothis even split can then be directly chosen from the ordered series.

In the ideal case, it is best to have partitions that are equally spacedwhere each partition has an equal number of data points. This would meanthat the ideal output distribution would have to be uniform over thetest range. This type of constraint can then be used as a criterion foroptimizing the selected input.

It is important to point out that the depth dependent partitioningscheme is directly dependent on the output of the system whereas theVolterra kernels were dependent on only the input. Therefore, anycomputational savings in the solution method for the Volterra kernel,where the input orthogonalization is pre-calculated, cannot be used. Theoutput partitioned kernels, however, generally have fewer parametersthan the Volterra kernels which means that the computation time islower.

Exemplary Partitioning Results

FIG. 15 illustrates an example of depth partitioned results for skin.FIG. 15A shows the breakpoints for the partitioning and FIG. 15B thekernels obtained from the estimate. The breakpoints, indicated byhorizontal dashed lines in FIG. 15A, were chosen by data density so theyare clustered heavily at the deeper values. Depth of indentation orposition in millimeter (mm) is shown on the vertical axis in FIG. 15A.One large partition exists at the very deepest values and one largepartition exists at the shallowest values.

As shown in FIG. 15B, the resulting kernels look like “impulse”responses as a function of depth. The deepest depth at 22.9 mm has avery heavily damped “impulse” response while the shallowest depth at16.9 mm is not heavily damped and shows more of the negative oscillatorypeak. Since the average value is still being estimated in thepartitioned kernel, there is some coupling of all the kernels with theaverage value.

Comparison with the Wiener Static Nonlinearity

To assess the goodness of fit of the depth partitioning technique, thevariance accounted for (VAF) and the Akaike Information Criterion (AIC),are calculated. The AIC is a method of measuring the negative impact ofthe entropy from additional parameters in estimation. In this data set,there are 15 partitions and the data was downsampled by 4. The estimatedmemory length was 40 samples. The VAF of this system is fairly good at92.2%. This is on par with results obtained using Volterra kernelsexcept with fewer parameters (600 parameters total for the partitionedtechnique versus 903 parameters for a second order Volterra with thesame memory length).

Direction Dependent Partitions

A slightly more advanced technique is the direction dependentpartitioning method. The idea is that as the probe moves into the skin,such as probe 102 when used in a configuration shown in FIG. 5A it willproduce different dynamics than when moving out of the skin. At lowdisplacements near the surface of the skin, one does not expect to seethis effect. At deeper positions into the skin where the compression isheavy, one expects to see a difference between the dynamics of pushingand pulling.

Linear system identification and most other nonlinear identificationtechniques including Volterra kernels assume no directionaldependencies. There are a class of system identification techniquesbased on characterizing hysteresis that are capable of looking atdirection dependent and history dependent dynamics. By partitioning theoutput, it is also possible to begin looking at the direction dependentdynamics as a function of depth. The construction equation that can beused for direction dependent partitioning is very similar toconstruction equation for the depth dependent, except that one mustaccount for direction, but for example a direction parameter D. Forexample, D is positive for one direction and negative for the otherdirection. As with before, the output y that is used to construct thepartitions can be low pass filtered to reduce noise. The reconstructionequation is also very similar to the depth dependent case except thenumber of kernels is twice as many as before since it covers both casesfor D.

With these two modified equations, it is possible to identify directiondependent dynamics. The results of this can be compared to depthdependent solutions. The mass as a function of depth does not change atall between pushing into the skin and pulling off the skin at the samedepth. The estimate for the damping and the spring constant, however,diverge as the probe goes deeper into the skin. This is in line withexpectations since it is expected that the skin would have more dampingresistance going into the skin than pulling off the skin. It is alsoexpected that pushing into the skin produces higher stiffness estimatesthan pulling off the skin at the same depth.

With the results from these two different types of partitioned kernels,one is able to tease apart some components of the dynamics using datafrom a single test. These methods tend to have a fewer number ofparameters and high VAF. In addition, they are more interpretable andeasier to compare than Volterra kernels. Conceptually, they are capableof measuring more dynamic features than simple static nonlinearities.Therefore, these partitioning techniques lie between the capabilities ofthe Volterra kernel and the static nonlinearities. Other partitioningschemes, including frequency dependence, can be used to group data inother configurations to obtain more general nonlinear dynamic estimates.

Input Generation and Real Time System Identification

In off-line system identification techniques the input generation, thesystem perturbation, and the system identification are three separateevents that are completed in series. In real-time system identificationthe system perturbation and system identification steps are completedtogether. Input generation can be combined with system perturbation andsystem identification in a single real-time algorithm.

Input Generation

Input generation is the key to stochastic system identification. Forsome techniques, such as for Wiener kernels, only certain input typeswill work. An input can be classified by its distribution (Gaussian,uniform, Rayleigh, etc. and by its spectral content (white or colored).Input signals can be generated with several different methods optimizedfor different situations. One type of input generation includestechniques that try to create inputs with a pre-specified distributionand spectrum (or autocorrelation).

When dealing with linear systems, the selection of the optimal input issimple and either Gaussian white inputs (or colored Gaussians) orstochastic binary signals are used. For nonlinear functions, however, itis much more difficult to make a general assessment of the optimal typeof input. For the types of nonlinearities found in biological tissues,where the nonlinearity is a function of the depth into the tissue, agood guess for an optimal input is an input that can generate a uniformoutput. A uniform output would explore the entire range of positions inthe nonlinearity and provide an equal number of values for all depths.In order to generate this optimal input, however, the systemnonlinearity must be known. It is possible to generate one input, assessthe output, and then iteratively generate new inputs until the desiredoutput is found. If this procedure is to be done by hand, it wouldrequire someone skilled in the art, it would be time consuming, and itwould require a large number of trials. For an instrument to be used ina clinical setting, the input can be generated in real time using systemfeedback thereby forgoing the disadvantages of creating inputs by hand.

Real Time System Identification

Adaptive and recursive algorithms can identify a system in real time.Each additional data point obtained can be used to modify the systemimpulse response immediately. These techniques are valuable insituations where the system changes or degrades over time. Withrecursive least squares (RLS), the additional data point is used tooptimally improve the overall estimate and information from all the datapoints is kept in the estimate. With adaptive least squares (ALS), someof the prior information is forgotten and the impulse response betterreflects the most recent information. Adaptive and recursive leastsquares algorithms are commonly used and multiple derivations exist andare optimized for different situations.

Input Generation with System Feedback

To properly identify the features of a nonlinear system where thenonlinearity is dependent on the output parameter, it is important thatsufficient information is gathered across the output range. There areseveral ways to achieve the optimal output that can be used to identifythe system including (but not limited to):

Real time input generation (RTIG) with output probability densityfunction (PDF) feedback: Measure the output of the system in real timeand adjust the offset of the input to meet the desired outputdistribution or PDF.

-   -   The optimal system output would be one in which the output        distribution or PDF is uniformly distributed across a desired        range for an input frequency of a sufficiently high cutoff. In        order to achieve this, it is possible to add a properly filtered        input range to an offset, which can be controlled to achieve the        proper output range. In this strategy, it is not important to        identify features of the system other than the output PDF. FIG.        16A illustrates an RTIG scheme with output PDF feedback.

Real time input generation (RTIG) with output PDF feedback and systemidentification: Measure and identify the system in real time and adjustthe offset of the input to meet the desired output PDF.

-   -   In this procedure, the system impulse response is identified and        the control system for determining the input offset is modulated        using a self tuning regulator with gain scheduling. FIG. 16B        illustrates an RTIG scheme with output PDF feedback and system        identification.

Output PDF Feedback

One of the most basic configurations for obtaining the desired outputPDF in real time is shown in FIG. 16A. First, a Gaussian or uniformwhite input can be generated and filtered with a controllable high orderdiscrete Butterworth filter to the desired input cutoff. This can thenbe added to a lower frequency control input that is commanded via afeedback loop. Information about the output distribution can be used toincrease or decrease the mean offset of the input in order to exploredifferent locations of the nonlinearity in the output. For the PDFshifting algorithm, it is necessary to know three things about thesystem: the desire input range, the desired output range, and theminimum system memory length. The desired input and output ranges areused to bound the input and output to ensure that the system does not gobeyond physical limits.

The minimum system memory length is used as the feedback rate. Since theinput of the system is a stochastic signal with an offset, the output ofthe system will have a mean value that is directly related to the inputoffset if the rate over which the mean is being taken is longer than theminimum system memory length. If the feedback rate is faster than theminimum system memory length, the relationship between the input offsetand the output mean begins to break down and the RTIG system can becomeunstable. In addition, to reduce the computational load for real-timeinput generation, a slower feedback rate is better. On the other hand,it is desirable to keep the test length short so a reasonably highfeedback rate is required. In practice, a minimum memory length of 0.025seconds (or 50 samples for a sampling rate of 2 kHz) can be used forfeedback control.

The control system for the PDF shifting algorithm shown in FIG. 16Aincludes the following steps during each feedback loop (once every 50samples for a sampling rate of 2 kHz):

1. First, split the output range into a number of bins. Output positionsthat have already been explored can be placed in these bins. The desiredoutput distribution function is a uniform so in order to explore all thebins evenly, the desired output for the next cycle would be the onewhich coincides with the bin with the least number of entries. Thisdesired location can be called y*. This idea can also be extended toother probability distributions by simply subtracting the entries frombins already filled with the desired distribution and finding the binwith the largest number of entries.

2. Next, determine the current output mean in terms of the number ofbins y.

3. Modulate the other input parameters:

(a) Determine the overall gain to the system: This parameter G_(o) candetermined from a general idea of the system stiffness or memory length.

(b) Determine the edge modulation: Uniform distributions have sharpedges. This requires that the gain be decreased as the output approachesthese edges.

Depending on the location of desired output location y* and the currentoutput location y, it is possible to increase or decrease the overallfeedback gain using the edge modulation parameter E_(m).

(c) Determine the stochastic input S(t): For a desired PDF with sharpedge contrast like a uniform distribution, it is generally a better ideato use a uniform stochastic input. Otherwise, a Gaussian distributionwill also work.

(d) Determine filter F(•) and gain G_(s) the stochastic input: Largergains will obtain more information about the system but can also causeinstability. The gain G_(s) can be scaled up to about 20 to 30% of thetotal input range and remain stable. The input filter is not necessaryfor algorithm functionality but can be implemented to clean up thesignals.

4. Combine the input with the following formula where x(t) is the newinput.

x(t)=x _(o) +G _(o) E _(m)(y*−y)+G _(s) F(S(t)  (8)

The first term x_(o) is the old input offset while the second term isthe offset change. The last term is the stochastic input. Thisconfiguration with the old input offset included in the output workswell when the system input and output are not zero mean. Additionalderivative or integral terms can be added to speed up the response ofthe system although additional speed could cause instability if theoutput mean can no longer be mapped to the input offset.

This algorithm can be improved with an identification step where thegain G_(o) is changed depending on the system stiffness. However, toobtain an output with the desired PDF, it is not necessary to identifythe system as long as the nonlinearity in the stiffness does not varyheavily. In addition, by not identifying the system, it is possible toobtain computational savings.

Using the output PDF feedback scheme shown FIG. 16A, simulation resultsfor the input and output time series can be obtained, for example, usinga linear system, a Wiener static nonlinear system, and a dynamicparameter nonlinearity (DPN) system. For exemplary results, the outputrange was constrained to vary between 0 and 6 mm and the input range wasallowed to vary from −5 to 25 N.

The algorithm is controlled by the output PDF, such as that shown inFIG. 7B, which can be analyzed. The linear system shows the ideal outputPDF that is very close to a uniform shape between 0 and 6 mm with sharpedge features. The Wiener and DPN systems, however, have shiftedprobability distribution functions. The main reason for this is that theunderlying nonlinearities in these two models have a variation instiffness for deeper positions. If the bin with the least number ofentries is near the stiffer regions, more and more input force isnecessary to reach that depth. Since the stiffness is unknown, thealgorithm does not adapt to increase gain for these stiffer locationsand therefore takes a longer time to reach those regions. Therefore,more entries occur in stiffer regions. Although the resultingdistribution is not ideal, it is still possible to control the input andoutput ranges with this algorithm.

The real time input generation scheme described herein has oneadditional interesting feature. Since the cyclic behavior is slow, theresulting predictions from the static nonlinearity are not as accurate.The static nonlinearity for the Wiener system is scattered and noisy.The static nonlinearity plot for the DPN system, on the other hand, isvery narrow and does not exhibit the effects of the cross dynamic termssince the output did not travel across large ranges fast enough.

Despite these drawbacks, the real time input generation without systemidentification works well for linear systems and systems with smallvariations in stiffness. The algorithms are also relatively fast and canbe completed in 1.3 seconds for a 5 second test making it a viablecandidate for real time input generation.

Output PDF Feedback with System Identification

When the identification is performed in real time, as is the case withthe recursive lease squares (RLS) and adaptive least squares (ALS)techniques, the identified system can only approach the optimalsolution. If the goal is to identify the system, then an ALS algorithmcan be used with some modification in conjunction with a real-time inputgeneration scheme. This idea is shown in FIG. 16B where the dataacquisition and identification steps are connected directly to the inputgeneration step. This methodology is distinct from the idea of using anobserver since the observer poles must be faster than the system poles.With this method, the feedback poles must be much slower than the systempoles.

This scheme is similar to the above-described PDF feedback scheme exceptfor the additional system identification step using an ALS algorithm.This algorithm utilizes information about the identified system todetermine the optimal input gains. The ALS system identification (ID)block, FIG. 16B, functions as follows once every feedback cycle:

1. Add new input and output sequences to an ALS algorithm. Note thatincluding the ALS algorithm may cause the RTIG system to go unstable ifthe ALS algorithm itself is unstable due to the choice of theidentification memory length (which does not have to be the same size asthe feedback memory length), impulse response fitting functions or thechange factor. The ALS system will adapt as the system goes to differentregions of the output nonlinearity.

2. Fit the most current impulse response to determine the stiffness,damping, and mass and associate these values with the average local binposition. This step can be the most computationally intensive and timeconsuming and will increases the total computation time to 7.5 secondsfor a 5 second test. This, however, can be easily shortened bydecreasing the number of parameters over which the fit is conducted ortrading of fitting accuracy for speed.

3. Scale the gain G_(o) and other input parameters with the identifiedsystem parameters at the average local bin position. The simplestversion of this can be done by scaling the input gain directly with theidentified local stiffness. More complex methods like pole placement canalso be used. This database of input parameters that depend on the localbin position can be passed on to the PDF shifting algorithm.

Exemplary results based on this algorithm show an immediate improvementin the cycling time which can be as low as 0.2 seconds for all thesystems marking an improvement in the time necessary to identify thenonlinear system. The plateau periods are also shorter and lesspronounced. Improvements also appear in the output PDF.

For example, when system identification is not used, it was onlypossible to achieve a uniform PDF for the linear system. With theaddition of depth dependent system identification via the ALS algorithm,it was possible to achieve near uniform PDFs for the same Wiener staticnonlinearity and dynamic parameter nonlinearity (DPN) systems. Inaddition, since the cycling speed increased, cross dynamic teams startto become visible in the static nonlinearity plot of the DPN system. Thestatic nonlinearity plot of the Wiener system, such as the one shown in10B, also becomes better defined.

The values from the ALS can also be used to identify the depth dependentparameters of the system. FIG. 17 shows identified parameters from realtime input generation with PDF feedback and adaptive least squares (ALS)algorithm. Results for three different systems, a linear system, aWiener static nonlinear system, and a dynamic parameter nonlinearity(DPN) system are shown. The parameters mass M, damping B, and stiffnessK, are plotted as a function of position, or depth, of the skinindentation. The linear system shows no variation in the threeparameters as a function of depth. The Wiener system shows how the threeparameters of mass, damping, and spring constant all change with depthin the same fashion. The DPN model shows how the three differentparameters can vary differently with depth.

There are two general caveats to using the ALS algorithm for systemidentification in this case. First, since the stochastic input range islarge, a lot of averaging across depths can occur which may skew theparameter estimates by averaging across depths. The ALS algorithm itselfmay additionally skew estimates across time. Better estimates maytherefore be obtained from the input and output data if an additionaloffline system identification technique is used after the data has beengathered.

Example Skin Studies—Indentation

Using a desktop version of device 100, the left anterior forearm 40 mmfrom the elbow and the left posterior forearm 40 mm from the wrist weretested for 7 right handed males between the ages of 18 and 28. Fivemeasurements were taken at each location using the same stochasticinput.

FIG. 18 shows plots of the means and standard deviations of fourmechanical properties of skin for two different positions on the leftarm. This figure shows significant differences across all four metrics,which are scaled mass M, scaled damping B, non-linear constant C₁, andnon-linear constant C₂. Variation between individuals is more than tentimes the variation between measurements for the same individual. InFIG. 18, the anterior position generally has a lower spring constantwhich boosts the scaled mass and the scaled damping. In addition, theanterior position has a larger compressible depth, which boostsestimates of C₁ and C₂. The means, standard deviations and the p-valuefrom an ANOVA study are shown in Table 1.

TABLE 1 Linear and nonlinear parameters and p-values of the leftanterior and posterior forearms of male, right-handed subjects. QuantityAnterior Forearm Posterior Forearm P-value Scaled Mass (s2) 0.0256 ±0.0042 0.0532 ± 0.0048 <0.0005 Scaled Damping (s) 5.02 ± 0.57 7.35 ±1.49 0.002 Nonlinear Constant 8.66 ± 1.03 11.99 ± 0.79  <0.0005 C1 (mm)Nonlinear Constant 0.170 ± 0.023 0.287 ± 0.069 0.001 C2 (1/N)

Note the normalized parameter estimates differ from the effectiveparameter estimates because the normalized parameters are achieved bydividing the impulse response by the DC compliance. This fixes thenormalized spring constant at 1000 N/m. The p-values show that thelinear and nonlinear constants are significantly different for the twopositions demonstrating that the device can easily differentiate betweenthe tissue properties at one site from those at another. In this table,all the metrics are statistically significant with p-values that aremuch less than 5%.

The posterior forearm has more damping as well as a larger compressibledepth, which are characteristic of more compliant tissue. Thedepth-dependant stiffness in the skin (C₁ and C₂) can be used todetermine parameters needed for needle free injection or used as ameasure of skin elasticity, which has been studied for applications incosmetics.

Device 100 can also be used to study larger populations of subjects toanalyze subgroups based on demographic similarities, such as age,gender, height, and weight. The device may be used to identifyparameters that provide indicators for body mass index (BMI) and helpidentify similarities across the population.

Example Skin Studies—Extension

Conventional extension is done in vitro but can also be conducted invivo. With an embodiment of the invention, such as device 100, twopositions on the skin were tested including the anterior proximalposition and posterior proximal position 40 mm from the elbow as shownin FIG. 19A. Such tests can be performed to determine the orientation ofLanger's lines, which are indicated schematically as thin lines in FIG.19A. The initial length of the area being tested was consistent for allfour configurations. The vertical and horizontal directions were testedand the localized linear results are shown in FIG. 19B. For lowerforces, there does not appear to be a preferential alignment of thestiffness of skin in a particular orientation. As the forces increasehowever, preferential alignment or increased stiffness begins to appearin the orientation of the Langer's lines in those regions. For theanterior proximal location, the vertical orientation is stiffer than thehorizontal orientation at larger forces. For the posterior proximallocation, the horizontal orientation is stiffer. These results are inline with expectations with higher stiffness or tension in the directionparallel to the Langer's lines.

Example Skin Studies—Surface Mechanics—Skin Care Products

The surface mechanics of skin includes several properties including skintexture, suppleness, and friction. When a probe, such as probe shown inthe inset of FIG. 5C, is placed on the skin, the skin can deformcontributing to changes in the measured damping (from skin energyabsorption, skin friction and other factors) as well as the springconstant (from skin suppleness, skin stiffness and other factors). It ispossible to measure the change in the spring constant of the skin aswell as the damping using the surface mechanics probe configuration ofdevice 100.

Commercial products such as hydrating lotions claim that they help holdmoisture in the skin. Two different products with two differenthydrating strategies were tested. The CHANEL HYDRAMAX+ACTIVE brandproduct is a non-greasy hydrating lotion which creates a protectivelayer on the skin. The VASELINE TOTAL MOISTURE brand cream is a moreviscous mixture. First, indentation studies were conducted using device100 with the localized linear method at different depths into the skin.A baseline measurement of the mechanical properties of skin was firstcollected using device 100 in an indentation configuration. An hourlater, one of the lotions was applied and the skin was tested again.Once a lotion was applied, the skin was allowed to rest for more thanone day before any further testing. The results of the indentation testsare shown in FIG. 20.

As shown in FIG. 20, there may be small differences between the use ofdifferent lotions and the control baseline. There is no significantdifference in the mass M or the damping B, but there is a spread in thespring constant K. With the CHANEL brand product, the spring constant ofthe skin actually increased slightly so that the skin became firmer. Forthe VASELINE brand product, the skin became softer and the springconstant was lower for every depth tested.

In terms of assessing the CHANEL brand product, however, an indentationtest may not be as useful as other testing configurations. The majoreffect of the product is to generate a hydrated layer in the skin, whichcan be characterized by a change in smoothness or in the surfacemechanics. Therefore, a surface mechanics test can be conducted using anembodiment, such as device 100, after applying the product on the skin.In an example surface mechanics measurement, the normal preload is 1 N.The dimensions of the probe in the example are 5 mm by 16 mm with anedge radius of 1.5 mm. The results of the example surface mechanics testare shown in FIG. 21. Each test was conducted ten times and the meansand standard deviations are shown.

The spring constant of the mechanical spring used in the system wassubtracted from the measurement of the compliance. Therefore, theremaining compliance is only the contribution of the skin, as measuredin a surface mechanics geometry by sliding a probe 102, such as shown inFIG. 5C, across the skin surface. FIG. 21 shows that a device, such asdevice 100, used with a nonlinear identification technique describedherein was able to detect and quantify a difference between thecompliance of the skin before and after the product was applied. TheCHANEL brand product made the skin more compliant in the slidingdirection, which means that the skin surface would appear to be smootherand suppler. In addition, the CHANEL brand product reduced the dampingof the skin, which indicates that it significantly reduced the roughnessor friction of the skin at the surface.

Embodiments of the invention, such as device 100, may be used to conductlong term testing with other types of products to fully assess theeffect of different lotions and creams immediately after application anda few minutes or hours after application. The data shown in FIG. 21,however, show that device 100 is consistent and sensitive enough tomeasure differences that can be felt by touch or palpation. In addition,device 100 can provide a clear quantitative assessment of skinproperties that can be compared directly.

The devices and methods disclosed herein can identify dynamic complianceof tissue and identify nonlinear dynamics of tissue using stochastictechniques, for example, using a Wiener static nonlinearity, localizedlinear testing, Volterra kernels, and partitioning techniques. Inaddition, the identification may be optimized with input generationtechniques. Other nonlinear system identification techniques, such aswavelets, subspace methods, and fuzzy logic models may also be used.

Embodiments of the invention provide an identification and computationaltechnique that is fast (e.g., on the order of 2 to 7 seconds), good ataccounting for variances in the data (e.g., VAF of more than 95%),readily interpretable, and capable of producing results that arerepeatable and specific. In addition, device 100 may be used todistinguish the change in skin properties, such as compliance, afterdehydration or after application of skin care or beauty care products.

Embodiments of the invention, such as device 100, are able to assessdynamic data to obtaining a more complete picture of tissue properties.In order to assess dynamic properties, a high bandwidth actuator systemcan be used. For example, a skin property identification geometry thatis conducive to high bandwidth actuation is indentation using a probecoupled to Lorentz force linear actuator.

The device 100 provides a platform technology for easy incorporation ofmultiple application heads (indentation, extension, surface mechanics)in order to measure multiple tissue parameters in several directions.Preferably, device 100 has a stroke of 32 mm and the capability ofdriving at least 15 N of force at a high bandwidth in order to measurethe nonlinear properties of skin in different configurations. To achievethese metrics, custom linear Lorentz force actuators can be used. Forclinical applications, device 100 can have a hand-held form factor thatis under 30 mm in diameter and 100 mm in length for the applicator bodyand may include integrated electronics. The design of device 100 mayinclude the use of low cost materials and scalable designs that caneasily lead to mass production, thereby making the technology readilyapplicable to commercialization.

Embodiments of the invention, such as device 100, can include integratedpower and sensor electronics with custom software, which can be used tocalibrate the system and assess the biological properties of skin andother biological tissues. Additional sensors capable of measuringnon-mechanical properties of skin such as blood reflow (using a lightsource and sensor), water content (using electrical contacts) and tissuelayer thickness (using ultrasound techniques) may be included.

The teachings of all patents, published applications and referencescited herein are incorporated by reference in their entirety.

While this invention has been particularly shown and described withreferences to example embodiments thereof, it will be understood bythose skilled in the art that various changes in form and details may bemade therein without departing from the scope of the inventionencompassed by the appended claims.

1. A method for testing the effect of a skin care product comprising:measuring a mechanical property of skin tissue using nonlinearstochastic system identification; applying the product to the skin;repeating the measurement of the mechanical property after theapplication of the product; and comparing the before and aftermeasurements to quantify the effect of the product.
 2. The method ofclaim 1, wherein measuring the mechanical property of the skin furthercomprises placing a probe against a surface of the skin, perturbing theskin with the probe using a stochastic sequence, and measuring theresponse of the skin to the perturbation.
 3. The method of claim 2,wherein measuring the response of the skin comprises measuringdisplacement of the skin.
 4. The method of claim 2, wherein perturbingthe skin comprises using a Lorentz force linear actuator.
 5. The methodof claim 2, wherein perturbing the skin comprises indenting the skinwith the probe.
 6. The method of claim 2, wherein perturbing the skincomprises extending the skin with the probe.
 7. The method of claim 2,wherein perturbing the skin comprises sliding the probe across the skinsurface.
 8. The method of claim 2, further comprising modeling the probeand skin as a system comprising a linear dynamic component and anon-linear static component.
 9. The method of claim 8, wherein modelingcomprises generating a non-parametric model.
 10. The method of claim 8,wherein modeling comprises generating a parametric model.
 11. The methodof claim 8, wherein the non-linear component includes a Wiener staticnonlinear system.
 12. The method of claim 8, wherein the linearcomponent includes a second order mechanical system.
 13. The method ofclaim 1, wherein the mechanical property is indicative of skincompliance.
 14. The method of claim 1, wherein the mechanical propertyis indicative of skin elasticity.
 15. The method of claim 1, wherein themechanical property is indicative of skin stiffness.
 16. The method ofclaim 1, wherein the mechanical property is indicative of skin damping.17. The method of claim 1, wherein the mechanical property is dependenton perturbation depth.
 18. The method of claim 1, wherein the mechanicalproperty is measured at a plurality of anatomical locations.